The focus of the study is based on binary sentiment classification on aspect level to develop a hybrid sentiment classification framework of WhatsApp MIMs (Mobile Instant Messages). It has been carried out into two phases i.e. training phase and testing phase. The training phase, 75% data is used for training dataset. Pre-processing techniques like tokenization, removing stop words, case normalization, removing punctuation and stemming are applied to acquire cleaner dataset to be used as input. The output is sent to the classifier after applying TF-IDF for feature weighting. In the second phase, the classifier is trial with 25% testing dataset. Bernoulli’s Naïve Bayesian classifier which is an improved form of traditional Naïve Bayesian classifier is used to classify sentiments. There are 417 messages in total where 244 and 173 are classified as positive and negative respectively. The proposed model has achieved satisfactory results up to 81.73% in comparison to base-line classification model by getting 12 points higher accuracy i.e. 69.23%.

There are many applications and circumstances where switching devices are required for proper operation, controlling and isolating the high power and low power systems. The most widely used switching devices are electromechanical relays and solid state relays. In this work, analysis and implementation of solid state relays over electromechanical relays with respect to instantaneous current supply inindustrial application is conducted and compared. For this purpose, an experimental setup is arranged for switching operation of electromechanical relays and solid-state relays.

IoT plays a major role in collecting the information from the sensing unit enclosing our environment due to alterations in the climate which led to the significance of environmental monitoring. This Paper presents a development of real time environmental monitoring and control system by utilizing Node MCU, DHT11 sensor, ACS712 current sensor, Thing Speak (Open IoT analytics service) and Blynk application. The main task of monitoring parameters (Humidity, temperature and power consumption) with real time sensors is done by Thing Speak continuously which has API (Application programming Interface) for gathering sensed data and enabling users to observe the monitored data in graphs for an interval of every 15 seconds

Smog is regarded as a dangerous situation for vehicles as the visibility of human fails and a person cannot drive smoothly and safely which can lead to severe accidents and routine disturbances. Hence, “Anti-Smog Radar Application for Vehicles” is proposed using Internet of Things technology which ensures the smooth flow of vehicles in smog and prevents accidents in time and cost-efficient manner by detecting and displaying the front objects with their important information (distance, position, velocity and size) on smart phone used by authenticated drivers. The radar application controls the hardware system to be deployed on vehicles through which the user can start, stop and set the hardware in particular direction. The hardware contains NodeMcu (Microcontroller with built-in Wi-Fi), Ultrasonic sensor and servo motor.